
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "170"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 134 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 129 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	102.363947	103.658876	inf	inf	32.656509	31.302299	89.600472	95.464980	nan	nan	nan	0.000000	0.000000
2459593	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.074959	2.538877	24.274150	17.019897	17.034690	14.288913	8.028395	4.024901	0.6562	0.6656	0.3230	4.883531	4.671862
2459592	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	3.237675	3.357189	56.198002	52.643698	8.428627	18.415167	-1.331755	1.803612	0.7343	0.7636	0.4033	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.878504	3.350739	28.286964	19.980374	14.801907	11.026227	-1.188638	0.049754	0.6265	0.6487	0.4033	4.231849	4.105615
2459590	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	361.315361	361.601119	inf	inf	31.419905	36.301011	212.635115	207.545223	nan	nan	nan	0.000000	0.000000
2459589	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	338.397506	339.154027	inf	inf	56.363633	59.067387	424.108781	432.229856	nan	nan	nan	0.000000	0.000000
2459588	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	225.663728	226.363328	inf	inf	23.396582	27.110528	19.135656	16.707727	nan	nan	nan	0.000000	0.000000
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	7.317546	4.090002	40.144431	29.032245	2.533847	2.556894	-0.487913	-1.295753	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.717593	0.680739	3.261871	1.228178	6.198461	5.455594	1.415714	0.005565	0.6281	0.6492	0.3850	4.848286	4.362335
2459585	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	34.861840	34.410103	23.851727	21.062088	7.432648	7.326266	-1.605223	5.765330	0.6855	0.7105	0.3157	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	19.875025	15.759129	40.816934	27.858156	10.370141	8.847208	21.190556	20.837446	0.5842	0.6113	0.4019	0.000000	0.000000
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.739530	2.850085	11.752246	7.864078	11.256119	7.558746	-0.592757	-0.020600	0.6181	0.6413	0.4267	-0.000000	-0.000000
2459582	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.312435	2.762129	10.771727	7.862377	14.094705	10.898524	-0.670842	-0.548257	0.6289	0.6509	0.4175	6.120401	5.160487
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.407379	4.210803	11.181133	8.476526	11.068226	3.035781	0.125848	0.089327	0.6291	0.6504	0.4179	3.830383	3.419963
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.824092	2.722392	12.291178	8.262144	12.129213	8.524386	-0.439372	0.516794	0.6333	0.6560	0.4140	6.897933	5.734003
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.713324	2.436136	14.140720	9.243090	13.426892	7.284860	-1.683428	3.124219	0.6283	0.6509	0.4191	10.487966	8.591527
2459578	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.754324	4.057116	31.244177	29.807508	8.271887	20.659058	6.131293	13.717098	0.7477	0.7830	0.4113	nan	nan
2459577	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.041966	4.140718	10.573406	7.949011	12.118289	6.220137	-0.929237	0.341641	0.6295	0.6518	0.4149	5.574012	4.985034
2459576	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.436497	3.251695	7.948069	5.917163	11.069843	9.089620	-0.500646	-0.793155	0.6451	0.6668	0.4247	4.474538	3.925944
2459575	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.345155	2.794763	8.477352	6.205157	9.593402	7.441908	6.082505	4.189787	0.7054	0.7243	0.3734	3.188954	2.844540
2459574	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.195242	3.754919	15.133093	11.565485	13.615954	10.924325	-0.471332	-0.565073	0.6497	0.6697	0.4195	-0.000000	-0.000000
2459572	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.882036	3.219452	7.953225	5.936707	9.385531	7.126627	-2.158991	-1.896466	0.6528	0.6739	0.4156	6.111446	5.108125
2459571	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.044510	3.747421	32.217976	31.046541	31.741168	49.211536	4.235902	11.852647	0.6863	0.7201	0.3919	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	806.977816	446.027117	inf	inf	-17940.016945	-9110.039074	-2437.569322	-1297.163151	nan	nan	nan	0.000000	0.000000
2459566	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.568324	3.307842	10.831250	8.256110	11.110703	8.607704	0.281117	0.507839	0.6505	0.6719	0.4268	0.000000	0.000000
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	0.751415	0.751415	0.674491	0.674491	0.881824	0.881824	0.311291	0.311291	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	2.673108	0.426898	2.523143	-0.113240	-0.182441	-1.787120	-0.421890	-0.258415	0.6585	0.6766	0.4248	1.838975	1.618543
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.297140	3.582965	22.959601	17.509670	11.930142	8.832128	1.312920	1.585653	0.6574	0.6769	0.4193	12.034356	10.944508
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.54%	1.530109	-0.309845	0.794933	-0.549310	-0.538428	-2.312174	-0.267551	-0.332619	0.6604	0.6828	0.4176	1.933403	1.716533
2459561	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	1.08%	1.564069	0.082970	0.931843	-0.235755	-0.247388	-1.416301	-0.421862	-0.490475	0.6618	0.6815	0.4277	1.832548	1.619188
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	1.08%	2.238700	0.540532	1.505779	0.259782	-0.024509	-0.895083	-0.741673	-0.707294	0.6609	0.6838	0.4144	1.949509	1.681098
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.256395	3.776707	12.409082	8.999920	12.562974	8.336820	-1.223458	0.256947	0.6674	0.6824	0.4193	8.233819	7.291750
2459557	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7997	0.8201	0.3126	nan	nan
2459556	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6642	0.6848	0.4255	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6629	0.6826	0.4240	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.871526	4.833848	16.898584	13.383240	17.857416	14.328946	1.150143	1.209752	0.6623	0.6823	0.4262	6.646798	6.595797
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	1.783911	0.122921	1.330575	-0.176266	-0.230309	-2.392297	-1.194308	-0.725648	0.6591	0.6844	0.4214	1.914571	1.662003
2459550	digital_ok	-	28.01%	28.01%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.5191	0.5434	0.2700	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.092653	3.461148	12.536983	9.476043	12.528364	9.246225	0.076458	0.981694	0.6585	0.6835	0.4303	3.882672	3.467219
2459542	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.915310	3.983998	24.861907	19.602461	0.396094	0.597627	-0.816233	-1.417510	0.7307	0.7430	0.3714	8.874498	7.846259
2459541	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459540	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	9.150366	6.003961	20.632359	16.144825	18.751853	15.911191	-2.795622	-1.854468	0.6759	0.6962	0.4538	0.000000	0.000000
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	98.66%	1.34%	2.238114	0.629775	2.157856	0.410406	1.371657	-1.910757	0.022507	0.698412	0.7555	0.7531	0.3897	0.981045	0.986500
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	97.77%	2.23%	1.728787	0.126386	1.247019	-0.479147	-0.532003	-2.394984	-1.184236	0.513537	0.7463	0.7426	0.3818	0.000000	0.000000
2459533	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.825410	3.433577	12.918367	8.547203	10.509189	6.708729	-1.790448	2.482852	0.6598	0.6876	0.4497	-0.000000	-0.000000
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.090089	3.977313	16.979096	11.009036	13.427548	8.708716	-1.973479	0.881132	0.6733	0.6968	0.4362	12.749201	9.382549
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	98.82%	1.18%	1.658241	0.171527	1.126903	-0.232486	-0.219153	-2.050765	-1.076349	0.025689	0.6881	0.7096	0.4318	0.876246	0.874371
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	4.823295	5.043521	30.633161	29.620273	7.240279	28.096801	4.260160	11.045558	0.8511	0.9015	0.2787	nan	nan
2459508	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.138830	1.076211	6.306164	5.470982	5.640692	3.546513	5.962512	6.210354	0.9016	0.9578	0.1953	0.000000	0.000000
2459505	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.943487	3.421483	8.646128	6.657138	0.816447	1.261017	-0.663260	2.454639	0.8846	0.8578	0.2048	18.071301	15.345374
2459503	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.154524	4.376616	8.811385	7.045140	14.674489	10.961337	0.031011	3.267607	0.0841	0.0522	0.0107	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 170 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 170, Node 15:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 170: 2459661

Antenna 170: 2459660

Antenna 170: 2459659

Antenna 170: 2459658

Antenna 170: 2459657

Antenna 170: 2459656

Antenna 170: 2459655

Antenna 170: 2459653

Antenna 170: 2459652

Antenna 170: 2459651

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Antenna 170: 2459649

Antenna 170: 2459648

Antenna 170: 2459647

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Antenna 170: 2459645

Antenna 170: 2459642

Antenna 170: 2459640

Antenna 170: 2459639

Antenna 170: 2459638

Antenna 170: 2459637

Antenna 170: 2459636

Antenna 170: 2459635

Antenna 170: 2459634

Antenna 170: 2459633

Antenna 170: 2459632

Antenna 170: 2459631

Antenna 170: 2459630

Antenna 170: 2459629

Antenna 170: 2459628

Antenna 170: 2459627

Antenna 170: 2459626

Antenna 170: 2459625

Antenna 170: 2459624

Antenna 170: 2459623

Antenna 170: 2459622

Antenna 170: 2459621

Antenna 170: 2459620

Antenna 170: 2459619

Antenna 170: 2459618

Antenna 170: 2459617

Antenna 170: 2459616

Antenna 170: 2459615

Antenna 170: 2459614

Antenna 170: 2459613

Antenna 170: 2459612

Antenna 170: 2459611

Antenna 170: 2459610

Antenna 170: 2459609

Antenna 170: 2459608

Antenna 170: 2459607

Antenna 170: 2459605

Antenna 170: 2459604

Antenna 170: 2459603

Antenna 170: 2459602

Antenna 170: 2459598

Antenna 170: 2459597

Antenna 170: 2459596

Antenna 170: 2459595

Antenna 170: 2459594

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Antenna 170: 2459589

Antenna 170: 2459588

Antenna 170: 2459587

Antenna 170: 2459586

Antenna 170: 2459585

Antenna 170: 2459584

Antenna 170: 2459583

Antenna 170: 2459582

Antenna 170: 2459581

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶